Loading UKF Input data

In [79]:
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))

import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()

my_cols=['c1','c2','c3','c4','c5','c6','c7','c8','c9','c10']
with open('../data/obj_pose-laser-radar-synthetic-input.txt') as f:
    table_input = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
    
table_input
Out[79]:
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10
0 L 0.312243 0.580340 1.477010e+15 6.000000e-01 0.600000 5.199937 0.000000 0.000000e+00 6.911322e-03
1 R 1.014892 0.554329 4.892807e+00 1.477010e+15 0.859997 0.600045 5.199747 1.796856e-03 3.455661e-04
2 L 1.173848 0.481073 1.477010e+15 1.119984e+00 0.600225 5.199429 0.005390 1.036644e-03 2.072960e-02
3 R 1.047505 0.389240 4.511325e+00 1.477010e+15 1.379955 0.600629 5.198979 1.077814e-02 2.073124e-03
4 L 1.650626 0.624690 1.477010e+15 1.639904e+00 0.601347 5.198392 0.017960 3.454842e-03 3.453479e-02
5 R 1.698300 0.298280 5.209986e+00 1.477010e+15 1.899823 0.602470 5.197661 2.693234e-02 5.181582e-03
6 L 2.188824 0.648739 1.477010e+15 2.159704e+00 0.604085 5.196776 0.037693 7.253069e-03 4.831816e-02
7 R 2.044382 0.276002 5.043867e+00 1.477010e+15 2.419540 0.606284 5.195728 5.023894e-02 9.668977e-03
8 L 2.655256 0.665980 1.477010e+15 2.679323e+00 0.609155 5.194504 0.064565 1.242892e-02 6.207101e-02
9 R 2.990916 0.217668 5.191807e+00 1.477010e+15 2.939043 0.612786 5.193090 8.066803e-02 1.553247e-02
10 L 3.012223 0.637046 1.477010e+15 3.198690e+00 0.617267 5.191470 0.098541 1.897914e-02 7.578466e-02
11 R 3.593878 0.135452 5.161753e+00 1.477010e+15 3.458253 0.622686 5.189627 1.181798e-01 2.276837e-02
12 L 3.893650 0.311793 1.477010e+15 3.717722e+00 0.629131 5.187542 0.139576 2.689958e-02 8.945044e-02
13 R 4.255547 0.164840 5.433327e+00 1.477010e+15 3.977082 0.636689 5.185194 1.627238e-01 3.137210e-02
14 L 4.309346 0.578564 1.477010e+15 4.236322e+00 0.645449 5.182560 0.187614 3.618523e-02 1.030597e-01
15 R 4.670263 0.148180 5.120847e+00 1.477010e+15 4.495424 0.655498 5.179618 2.142382e-01 4.133822e-02
16 L 4.351431 0.899174 1.477010e+15 4.754374e+00 0.666921 5.176340 0.242587 4.683024e-02 1.166039e-01
17 R 5.251417 0.127164 4.825914e+00 1.477010e+15 5.013155 0.679804 5.172700 2.726487e-01 5.266044e-02
18 L 5.518935 0.648233 1.477010e+15 5.271746e+00 0.694234 5.168671 0.304413 5.882788e-02 1.300744e-01
19 R 5.267293 0.121683 5.423506e+00 1.477010e+15 5.530128 0.710295 5.164221 3.378677e-01 6.533161e-02
20 L 6.022003 0.708619 1.477010e+15 5.788279e+00 0.728071 5.159319 0.372999 7.217058e-02 1.434628e-01
21 R 5.905749 0.063300 4.879680e+00 1.477010e+15 6.046176 0.747646 5.153933 4.097925e-01 7.934372e-02
22 L 6.342486 0.948833 1.477010e+15 6.303794e+00 0.769103 5.148029 0.448233 8.684990e-02 1.567606e-01
23 R 6.673922 0.125614 5.006870e+00 1.477010e+15 6.561105 0.792523 5.141571 4.883049e-01 9.468793e-02
24 L 6.782143 0.714036 1.477010e+15 6.818081e+00 0.817988 5.134523 0.529990 1.028566e-01 1.699593e-01
25 R 7.318441 0.086292 4.649107e+00 1.477010e+15 7.074691 0.845578 5.126847 5.732691e-01 1.113545e-01
26 L 7.137350 0.957217 1.477010e+15 7.330903e+00 0.875372 5.118505 0.618123 1.201805e-01 1.830507e-01
27 R 8.124935 0.101047 5.464240e+00 1.477010e+15 7.586684 0.907449 5.109456 6.645307e-01 1.293330e-01
28 L 7.805334 0.719126 1.477010e+15 7.841995e+00 0.941886 5.099659 0.712469 1.388107e-01 1.960265e-01
29 R 8.450951 0.104862 4.750535e+00 1.477010e+15 8.096800 0.978758 5.089074 7.619151e-01 1.486120e-01
... ... ... ... ... ... ... ... ... ... ...
470 L -14.408830 10.222010 1.477010e+15 -1.447663e+01 10.527600 5.092432 0.762418 1.486120e-01 -1.960265e-01
471 R 17.434840 2.493576 -3.802615e+00 1.477010e+15 -14.221830 10.564470 5.102928 7.129259e-01 1.388107e-01
472 L -13.804860 10.938330 1.477010e+15 -1.396652e+01 10.598910 5.112633 0.664944 1.293330e-01 -1.830507e-01
473 R 17.198060 2.463710 -3.313897e+00 1.477010e+15 -13.710730 10.630990 5.121588 6.184955e-01 1.201805e-01
474 L -13.355600 10.551700 1.477010e+15 -1.345452e+01 10.660780 5.129834 0.573603 1.113545e-01 -1.699593e-01
475 R 16.861570 2.445369 -3.743524e+00 1.477010e+15 -13.197910 10.688370 5.137411 5.302878e-01 1.028566e-01
476 L -12.959060 11.009890 1.477010e+15 -1.294094e+01 10.713830 5.144357 0.488570 9.468793e-02 -1.567606e-01
477 R 16.633020 2.398815 -3.455295e+00 1.477010e+15 -12.683620 10.737260 5.150712 4.484670e-01 8.684990e-02
478 L -12.371330 10.609140 1.477010e+15 -1.242601e+01 10.758710 5.156511 0.409998 7.934372e-02 -1.434628e-01
479 R 16.174360 2.400763 -3.361253e+00 1.477010e+15 -12.168110 10.778290 5.161789 3.731774e-01 7.217058e-02
480 L -11.945090 11.051500 1.477010e+15 -1.190996e+01 10.796060 5.166582 0.338022 6.533161e-02 -1.300744e-01
481 R 15.512470 2.356317 -3.456733e+00 1.477010e+15 -11.651580 10.812120 5.170921 3.045457e-01 5.882788e-02
482 L -11.454830 10.940670 1.477010e+15 -1.139299e+01 10.826550 5.174838 0.272761 5.266044e-02 -1.166039e-01
483 R 15.467450 2.394427 -4.449697e+00 1.477010e+15 -11.134210 10.839440 5.178363 2.426814e-01 4.683024e-02
484 L -10.600860 10.682250 1.477010e+15 -1.087526e+01 10.850860 5.181525 0.214317 4.133822e-02 -1.030597e-01
485 R 15.198470 2.347791 -3.728088e+00 1.477010e+15 -10.616150 10.860910 5.184351 1.876789e-01 3.618523e-02
486 L -10.414230 10.874650 1.477010e+15 -1.035691e+01 10.869670 5.186866 0.162776 3.137210e-02 -8.945044e-02
487 R 14.970070 2.293741 -2.909210e+00 1.477010e+15 -10.097550 10.877230 5.189095 1.396181e-01 2.689958e-02
488 L -9.834446 11.103210 1.477010e+15 -9.838085e+00 10.883670 5.191060 0.118212 2.276837e-02 -7.578466e-02
489 R 14.527780 2.348663 -3.348510e+00 1.477010e+15 -9.578521 10.889090 5.192781 9.856635e-02 1.897914e-02
490 L -9.306602 11.034760 1.477010e+15 -9.318874e+00 10.893570 5.194279 0.080686 1.553247e-02 -6.207101e-02
491 R 13.864840 2.298530 -3.203442e+00 1.477010e+15 -9.059154 10.897200 5.195570 6.457867e-02 1.242892e-02
492 L -8.788986 11.070660 1.477010e+15 -8.799372e+00 10.900070 5.196670 0.050248 9.668977e-03 -4.831816e-02
493 R 13.370450 2.293074 -3.215725e+00 1.477010e+15 -8.539535 10.902270 5.197594 3.769917e-02 7.253069e-03
494 L -8.620445 10.657660 1.477010e+15 -8.279654e+00 10.903890 5.198353 0.026936 5.181582e-03 -3.453479e-02
495 R 13.645960 2.189595 -2.987211e+00 1.477010e+15 -8.019735 10.905010 5.198959 1.796166e-02 3.454842e-03
496 L -7.519712 11.000450 1.477010e+15 -7.759787e+00 10.905730 5.199421 0.010779 2.073124e-03 -2.072960e-02
497 R 12.885600 2.169303 -2.779369e+00 1.477010e+15 -7.499815 10.906130 5.199745 5.390285e-03 1.036644e-03
498 L -7.156314 10.815040 1.477010e+15 -7.239828e+00 10.906310 5.199937 0.001797 3.455661e-04 -6.911322e-03
499 R 13.269100 2.161844 -2.405718e+00 1.477010e+15 -6.979831 10.906360 5.200000 -7.848735e-15 -1.509372e-15

500 rows × 10 columns

Loading UKF Output data¶

In [80]:
import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()

my_cols=['sensor','px_est','py_est','vx_est','vy_est','px_meas','py_meas','px_gt','py_gt','vx_gt','vy_gt','NIS','RMSE x','RMSE y','RMSE vx','RMSE vy','acceleration_x','acceleration_y','dt']
with open('../data/obj_pose-laser-radar-ukf-output.txt') as f:
    table_ukf_output = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
    
table_ukf_output
Out[80]:
sensor px_est py_est vx_est vy_est px_meas py_meas px_gt py_gt vx_gt vy_gt NIS RMSE x RMSE y RMSE vx RMSE vy acceleration_x acceleration_y dt
0 L 0.312243 0.580340 5.000000 0.000000 0.312243 0.580340 0.600000 0.600000 5.199940 0.000000 0.000000 0.287757 0.019660 0.199937 0.000000 0.000000 0.000000 0.00
1 R 0.741466 0.499762 5.378667 0.134583 0.862916 0.534212 0.859997 0.600045 5.199747 0.001797 2.306294 0.220061 0.072261 0.189720 0.093894 -0.003796 0.035937 0.05
2 L 1.059574 0.502753 5.640649 -0.523301 1.173848 0.481073 1.119984 0.600225 5.199429 0.005390 0.923797 0.183033 0.081535 0.298140 0.314720 -0.006361 0.071862 0.05
3 R 1.256753 0.513805 5.057700 -0.075615 0.969149 0.397513 1.379955 0.600629 5.198979 0.010778 1.674388 0.170060 0.082889 0.267686 0.275957 -0.009003 0.107764 0.05
4 L 1.552765 0.528905 5.145473 -0.119564 1.650626 0.624690 1.639904 0.601347 5.198392 0.017960 1.119197 0.157019 0.080908 0.240592 0.254371 -0.011740 0.143631 0.05
5 R 1.810837 0.546598 5.233300 0.198646 1.623309 0.499091 1.899823 0.602470 5.197661 0.026932 1.210026 0.147870 0.077300 0.220111 0.242559 -0.014620 0.179453 0.05
6 L 2.098481 0.574277 5.264816 0.346316 2.188824 0.648739 2.159704 0.604085 5.196776 0.037693 0.777820 0.138843 0.072447 0.205399 0.253055 -0.017700 0.215218 0.05
7 R 2.314849 0.632081 5.104791 0.787543 1.967008 0.557117 2.419540 0.606284 5.195728 0.050239 1.914186 0.135047 0.068379 0.194805 0.352114 -0.020962 0.250914 0.05
8 L 2.586150 0.670757 5.118279 0.888579 2.655256 0.665980 2.679323 0.609155 5.194504 0.064565 0.272137 0.131057 0.067660 0.185413 0.430874 -0.024481 0.286530 0.05
9 R 2.857759 0.667057 5.190672 0.667385 2.920341 0.645898 2.939043 0.612786 5.193090 0.080668 0.436781 0.126961 0.066443 0.175900 0.448900 -0.028276 0.322052 0.05
10 L 3.096430 0.685079 5.166228 0.657451 3.012223 0.637046 3.198690 0.617267 5.191470 0.098541 0.523536 0.124917 0.066568 0.167887 0.459989 -0.032396 0.357469 0.05
11 R 3.386724 0.582993 5.248301 -0.119247 3.560959 0.485311 3.458253 0.622686 5.189627 0.118180 3.230599 0.121369 0.064756 0.161629 0.445707 -0.036860 0.392767 0.05
12 L 3.693801 0.490449 5.268319 -0.683098 3.893650 0.311793 3.717722 0.629131 5.187542 0.139576 4.283482 0.116796 0.073145 0.156896 0.485216 -0.041704 0.427932 0.05
13 R 3.973959 0.560792 5.360751 -0.206214 4.197862 0.698311 3.977082 0.636689 5.185194 0.162724 2.697666 0.112550 0.073345 0.158302 0.477850 -0.046959 0.462948 0.05
14 L 4.251971 0.558035 5.374167 -0.206539 4.309346 0.578564 4.236322 0.645449 5.182560 0.187614 0.201669 0.108809 0.074366 0.160737 0.472731 -0.052681 0.497804 0.05
15 R 4.511635 0.591455 5.329997 -0.042689 4.619083 0.689510 4.495424 0.655498 5.179618 0.214238 0.971787 0.105432 0.073763 0.160109 0.462205 -0.058842 0.532484 0.05
16 L 4.705034 0.694538 5.198410 0.398556 4.351431 0.899174 4.754374 0.666921 5.176340 0.242587 9.512195 0.102982 0.071873 0.155421 0.449998 -0.065556 0.566968 0.05
17 R 4.957839 0.690895 5.136764 0.329040 5.209015 0.665990 5.013155 0.679804 5.172700 0.272649 2.034607 0.100926 0.069897 0.151280 0.437521 -0.072803 0.601242 0.05
18 L 5.262900 0.684604 5.208869 0.257305 5.518935 0.648233 5.271746 0.694234 5.168671 0.304413 3.567801 0.098255 0.068069 0.147533 0.425989 -0.080576 0.635290 0.05
19 R 5.522686 0.690887 5.248742 0.249500 5.228345 0.639362 5.530128 0.710295 5.164221 0.337868 1.512094 0.095781 0.066487 0.145034 0.415672 -0.089006 0.669090 0.05
20 L 5.821327 0.702001 5.298814 0.250180 6.022003 0.708619 5.788279 0.728071 5.159319 0.372999 2.120329 0.093751 0.065134 0.144775 0.406539 -0.098038 0.702624 0.05
21 R 6.064652 0.634640 5.214659 -0.000309 5.893921 0.373584 6.046176 0.747646 5.153933 0.409793 4.274659 0.091680 0.068044 0.142038 0.406702 -0.107717 0.735872 0.05
22 L 6.323675 0.735937 5.201082 0.304900 6.342486 0.948833 6.303794 0.769103 5.148029 0.448233 2.990267 0.089761 0.066907 0.139356 0.398883 -0.118084 0.768816 0.05
23 R 6.574407 0.766852 5.154574 0.362167 6.621337 0.836138 6.561105 0.792523 5.141571 0.488305 0.466965 0.087913 0.065708 0.136447 0.391333 -0.129156 0.801432 0.05
24 L 6.825061 0.763275 5.148383 0.317124 6.782143 0.714036 6.818081 0.817988 5.134523 0.529990 0.250386 0.086148 0.065303 0.133719 0.385783 -0.140963 0.833696 0.05
25 R 7.067966 0.750183 5.051952 0.246883 7.291210 0.630741 7.074691 0.845578 5.126847 0.573269 3.142548 0.084485 0.066712 0.131943 0.383668 -0.153522 0.865588 0.05
26 L 7.289928 0.825731 5.000150 0.419245 7.137350 0.957217 7.330903 0.875372 5.118505 0.618123 2.376548 0.083280 0.066159 0.131464 0.378437 -0.166836 0.897081 0.05
27 R 7.583298 0.838742 5.130212 0.418650 8.083490 0.819605 7.586684 0.907449 5.109456 0.664531 4.692094 0.081782 0.066251 0.129155 0.374511 -0.180979 0.928150 0.05
28 L 7.836205 0.817478 5.129933 0.329613 7.805334 0.719126 7.841995 0.941886 5.099659 0.712469 0.662716 0.080367 0.069077 0.127033 0.374802 -0.195942 0.958772 0.05
29 R 8.085638 0.836215 5.057846 0.340612 8.404531 0.884557 8.096800 0.978758 5.089074 0.761915 2.452605 0.079042 0.072731 0.125028 0.376445 -0.211697 0.988916 0.05
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
469 R -14.677060 10.427220 5.092820 0.610261 -14.724010 9.635065 -14.730890 10.488220 5.081101 0.813394 6.472394 0.073334 0.083481 0.179077 0.193616 0.244179 -1.048685 0.05
470 L -14.434250 10.399390 5.065840 0.433725 -14.408830 10.222010 -14.476630 10.527600 5.092432 0.762418 1.858912 0.073282 0.083602 0.178891 0.194003 0.226622 -1.019527 0.05
471 R -14.174870 10.423310 5.073041 0.361832 -13.900490 10.523780 -14.221830 10.564470 5.102928 0.712926 0.564942 0.073237 0.083766 0.178706 0.194470 0.209923 -0.989835 0.05
472 L -13.867430 10.567220 5.196731 0.503086 -13.804860 10.938330 -13.966520 10.598910 5.112633 0.664944 8.551981 0.073301 0.083690 0.178559 0.194407 0.194101 -0.959641 0.05
473 R -13.618620 10.619030 5.129890 0.509269 -13.395610 10.785680 -13.710730 10.630990 5.121588 0.618495 2.733645 0.073346 0.083603 0.178371 0.194266 0.179100 -0.928967 0.05
474 L -13.366610 10.621580 5.123786 0.404472 -13.355600 10.551700 -13.454520 10.660780 5.129834 0.573603 0.286517 0.073379 0.083534 0.178184 0.194217 0.164919 -0.897849 0.05
475 R -13.106140 10.646120 5.134246 0.347521 -12.937370 10.813740 -13.197910 10.688370 5.137411 0.530288 0.236242 0.073423 0.083469 0.177996 0.194193 0.151539 -0.866306 0.05
476 L -12.849150 10.736700 5.158253 0.409999 -12.959060 11.009890 -12.940940 10.713830 5.144357 0.488570 4.760573 0.073466 0.083388 0.177811 0.194023 0.138922 -0.834364 0.05
477 R -12.589040 10.782710 5.145145 0.409532 -12.251760 11.249520 -12.683620 10.737260 5.150712 0.448467 1.906165 0.073517 0.083327 0.177625 0.193828 0.127096 -0.802052 0.05
478 L -12.352130 10.754690 5.106111 0.267607 -12.371330 10.609140 -12.426010 10.758710 5.156511 0.409998 1.280192 0.073517 0.083240 0.177454 0.193735 0.115976 -0.769390 0.05
479 R -12.101170 10.785620 5.070662 0.257855 -11.935200 10.916080 -12.168110 10.778290 5.161789 0.373177 1.066952 0.073504 0.083154 0.177318 0.193605 0.105562 -0.736402 0.05
480 L -11.851340 10.848830 5.080897 0.288161 -11.945090 11.051500 -11.909960 10.796060 5.166582 0.338022 2.696551 0.073477 0.083102 0.177177 0.193417 0.095863 -0.703106 0.05
481 R -11.578180 10.868330 5.103909 0.253192 -10.970320 10.967630 -11.651580 10.812120 5.170921 0.304546 2.769714 0.073476 0.083055 0.177019 0.193230 0.086775 -0.669528 0.05
482 L -11.346520 10.884970 5.071086 0.207081 -11.454830 10.940670 -11.392990 10.826550 5.174838 0.272761 0.784994 0.073431 0.083012 0.176899 0.193053 0.078344 -0.635686 0.05
483 R -11.076970 10.834740 5.184942 -0.013123 -11.347200 10.511090 -11.134210 10.839440 5.178363 0.242681 8.648595 0.073401 0.082926 0.176716 0.193204 0.070496 -0.601600 0.05
484 L -10.781720 10.813400 5.234992 -0.110717 -10.600860 10.682250 -10.875260 10.850860 5.181525 0.214317 2.620697 0.073448 0.082858 0.176551 0.193568 0.063248 -0.567286 0.05
485 R -10.526370 10.809860 5.219823 -0.168603 -10.656250 10.836870 -10.616150 10.860910 5.184351 0.187679 0.184757 0.073485 0.082806 0.176376 0.194043 0.056515 -0.532764 0.05
486 L -10.292590 10.806590 5.176848 -0.224080 -10.414230 10.874650 -10.356910 10.869670 5.186866 0.162776 1.029735 0.073468 0.082770 0.176196 0.194635 0.050297 -0.498052 0.05
487 R -10.051240 10.849690 5.072957 -0.136061 -9.904133 11.225470 -10.097550 10.877230 5.189095 0.139618 6.939873 0.073422 0.082694 0.176094 0.194835 0.044584 -0.463164 0.05
488 L -9.789078 10.895110 5.099642 -0.099600 -9.834446 11.103210 -9.838085 10.883670 5.191060 0.118212 2.493017 0.073381 0.082611 0.175962 0.194885 0.039301 -0.428114 0.05
489 R -9.562141 10.875210 5.037344 -0.166426 -10.195030 10.349770 -9.578521 10.889090 5.192781 0.098566 3.851388 0.073310 0.082529 0.175923 0.195054 0.034418 -0.392921 0.05
490 L -9.299057 10.901240 5.063241 -0.156556 -9.306602 11.034760 -9.318874 10.893570 5.194279 0.080686 1.001673 0.073240 0.082446 0.175843 0.195149 0.029964 -0.357597 0.05
491 R -9.057308 10.880530 5.020085 -0.209822 -9.222603 10.352650 -9.059154 10.897200 5.195570 0.064579 2.584013 0.073166 0.082366 0.175842 0.195343 0.025816 -0.322156 0.05
492 L -8.790263 10.911200 5.055213 -0.185163 -8.788986 11.070660 -8.799372 10.900070 5.196670 0.050248 1.430197 0.073093 0.082284 0.175779 0.195432 0.022001 -0.286612 0.05
493 R -8.553499 10.869990 5.009641 -0.274073 -8.839141 10.031880 -8.539535 10.902270 5.197594 0.037699 6.095158 0.073022 0.082213 0.175805 0.195738 0.018482 -0.250978 0.05
494 L -8.386322 10.796910 4.854779 -0.418184 -8.620445 10.657660 -8.279654 10.903890 5.198353 0.026936 4.524035 0.073105 0.082271 0.176305 0.196561 0.015173 -0.215265 0.05
495 R -8.140694 10.803900 4.833407 -0.397714 -7.915434 11.115670 -8.019735 10.905010 5.198959 0.017962 1.831414 0.073233 0.082313 0.176890 0.197247 0.012121 -0.179485 0.05
496 L -7.800433 10.847610 5.007687 -0.329395 -7.519712 11.000450 -7.759787 10.905730 5.199421 0.010779 6.212137 0.073182 0.082271 0.176921 0.197639 0.009241 -0.143652 0.05
497 R -7.543596 10.844730 4.982779 -0.311995 -7.259867 10.645800 -7.499815 10.906130 5.199745 0.005390 2.598313 0.073135 0.082235 0.177011 0.197952 0.006485 -0.107776 0.05
498 L -7.263648 10.833610 5.032451 -0.335603 -7.156314 10.815040 -7.239828 10.906310 5.199937 0.001797 0.670869 0.073069 0.082217 0.176992 0.198329 0.003834 -0.071867 0.05

499 rows × 19 columns

Loading Visualization

In [81]:
import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()

my_cols=['sensor','px_est','py_est','vx_est','vy_est','px_meas','py_meas','px_gt','py_gt','vx_gt','vy_gt','NIS','RMSE x','RMSE y','RMSE vx','RMSE vy','acceleration_x','acceleration_y','dt']
with open('../data/obj_pose-laser-radar-ukf-output.txt') as f:
    table_ukf_output = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
    
#table_ukf_output
In [82]:
import plotly.offline as py
from plotly.graph_objs import *


#Measurements
trace2 = Scatter(
    x=table_ukf_output['px_meas'],
    y=table_ukf_output['py_meas'],
    xaxis='x2',
    yaxis='y2',
    name = 'Measurements',
    #mode = 'markers'
)

#estimations
trace1 = Scatter(
    x=table_ukf_output['px_est'],
    y=table_ukf_output['py_est'],
    xaxis='x2',
    yaxis='y2',
    name='UKF- Estimate',
    mode = 'markers'       
)

#Ground Truth
trace3 = Scatter(
    x=table_ukf_output['px_gt'],
    y=table_ukf_output['py_gt'],
    xaxis='x2',
    yaxis='y2',
    name = 'Ground Truth',
    mode = 'markers'      
)

data = [trace1, trace2, trace3]

layout = Layout(
    xaxis2=dict(
   
        anchor='x2',
        title='px'
    ),
    yaxis2=dict(
    
        anchor='y2',
        title='py'
    )
)

fig = Figure(data=data, layout=layout)
py.iplot(fig, filename= 'UKF')
In [ ]:
 
In [83]:
import plotly.offline as py
from plotly.graph_objs import *


#estimations
trace1 = Scatter(
    x=table_ukf_output['px_est'],
    y=table_ukf_output['py_est'],
    xaxis='x2',
    yaxis='y2',
    name='UKF- Estimate'
)

#Measurements
trace2 = Scatter(
    x=table_ukf_output['px_meas'],
    y=table_ukf_output['py_meas'],
    xaxis='x2',
    yaxis='y2',
    name = 'Measurements',
    #mode = 'markers'
)

#Measurements
trace3 = Scatter(
    x=table_ukf_output['px_gt'],
    y=table_ukf_output['py_gt'],
    xaxis='x2',
    yaxis='y2',
    name = 'Ground Truth'
)

data = [trace1, trace2, trace3]

layout = Layout(
    xaxis2=dict(
   
        anchor='x2',
        title='px'
    ),
    yaxis2=dict(
    
        anchor='y2',
        title='py'
    )
)

fig = Figure(data=data, layout=layout)
py.iplot(fig, filename= 'UKF')
In [ ]: